Trustworthiness Requirements for Manufacturing Cyber-Physical Systems

Distributed manufacturing operations include cyber-physical systems vulnerable to cyber-attacks. Long time not considered a priority, cybersecurity jumped to the forefront of manufacturing concerns due to the need to network together legacy, newer equipment, and entire operation centers. This paper proposes trustworthiness solutions for integrated manufacturing physical-cyber worlds, where trustworthiness is defined to complement system dependability requirements with cybersecurity requirements, such that the resulting manufacturing cyber-physical system delivers services that can justifiably be trusted. Acknowledging the inevitability of cyber-attacks, the paper models the cybersecurity component using the resilient systems framework, where system resilience is viewed as preservation of a required state of cybersecurity

The Relation Between Magnetospheric State Parameters and the Occurrence of Plasma Depletion Events in the Night-Time Mid-Latitude F-Region

Studies using all-sky imagers have revealed the presence of various ionospheric irregularities in the night-time mid-latitude F-region. The most prevalent and well known of these are the Medium Scale Traveling Ionospheric Disturbances (MSTIDs) that usually occur when the geomagnetic activity is low, and mid-latitude spread-F plumes that are often observed when the geomagnetic activity is high. The inverse and direct relations between geomagnetic activity (particularly Kp) and the occurrence rate of MSTIDs and midlatitude plumes, respectively, have been observed by several studies using different instruments. In order to understand the underlying causes of these two relations, it is illuminating to better characterize the occurrence of MSTIDs and plumes using multiple magnetospheric state parameters. Here we statistically compare multiple geomagnetic driver and response parameters (such as Kp, AE, Dst, and solar wind parameters) with the occurrence rates of night-time MSTIDs and plumes observed using an all-sky imager at Arecibo Observatory (AO) between 2003 and 2008. The results not only allow us to better distinguish MSTIDs and plumes, but also shed further light on the generation mechanism and electrodynamics of these two different phenomena occurring at night-time in the mid-latitude F-region

Multi-Instrument Observations of an MSTID over Arecibo Observatory

The Penn State All-Sky Imager (PSASI) at Arecibo Observatory provides planar horizontal context to the vertical ionospheric profiles obtained by the Incoherent Seatter Radar (TSR). Electric field measurements from the Communication/Navigation Outage Forecast System (C/NOFS) satellite are mapped down geomagnetic field lines to the height of the airglow layer; allowing multi-instrument studies of field-aligned irregularities with radar, imager, and satellite. A Medium-Scale Traveling Ionospheric Disturbance (MSTID) was observed during such a conjunction near the December solstice of 2009

Effect of vitamin E on the blood-brain barrier permeability in aged rats with ptz-induced convulsions

The effect of vitamin E on the blood-brain barrier (BBB) permeability was studied under conditions of pentylenetetrazole (PTZ)-induced convulsions in aged (23- to 24-month-old) male albino rats; Evans Blue was used as a tracer. The BBB permeability was found to considerably increase in rats with PTZ-evoked seizures; the Evans Blue contents in the left and right hemispheres and cerebellum + brainstem region were significantly higher than those in the control. Vitamin E used in the dose of 70 mg/kg exerted practically no beneficial effect on the increased BBB permeability in rats with seizures, while a greater dose of vitamin E (700 mg/kg) exerted a significant protective effect, especially with respect to the cerebellum + brainstem regions (P < 0.01). The seizure-related rise in the arterial blood pressure was also smaller in the latter experimental group. Thus, our observations confirm the importance of the dose of vitamin E as a protective factor for the BBB permeability and demonstrate that the dose dependence of this antioxidant in aged animals dif fers from that in younger or ganisms.На старих (вік 23–24 місяці) самцях білих щурів досліджували вплив вітаміну Е на розлади проникності гематоенцефалічного бар’єра (ГЕБ), пов’язані з розвитком судомної активності, котра була індукована введенням пентилентетразолу (ПТЗ); як трасер використовували барвник Еванс блакитний. У тварин із ПТЗ-викликаними судомами проникність ГЕБ значно збільшувалася; вміст барвника в тканинах лівої та правої мозкових півкуль, мозочка та стовбура мозку вірогідно перевищував відповідні значення в контролі. Вітамін Е в дозі 70 мг/кг практично не справляв позитивного впливу на збільшену проникність ГЕБ у тварин із судомною активністю. Застосування ж вітаміну Е в більших дозах (700 мг/кг) забезпечувало істотний протективний ефект, особливо щодо зразків тканин мозочка та стовбура мозку (P < 0.01). Підвищення артеріального кров’яного тиску, пов’язане з розвитком судом, в останній з експериментальних серій було помірнішим, ніж у двох інших серіях. Отже, наші спостереження підтверджують важливість дозування вітаміну Е як протективного фактора щодо проникності ГЕБ та вказують на те, що залежність від дози цього вітаміну, використовуваного як антиоксидант, у старих тварин істотно відрізняється від такої у молодших організмів

Effect of different segmentation methods using optical satellite imagery to estimate fuzzy clustering parameters for Sentinel-1A SAR images

Optical and SAR data are efficient data sources for shoreline monitoring. The processing of SAR data such as feature extraction is not an easy task since the images have totally different structure than optical imagery. Determination of threshold value is a challenging task for SAR data. In this study, SENTINEL-2A optical data was used as ancillary data to predict fuzzy membership parameters for segmentation of SENTINEL-1A SAR data to extract shoreline. SENTINEL-2A and SENTINEL-1A satellite images used were taken in September 9, 2016 and September 13, 2016 respectively. Three different segmentation algorithms which are selected from object, learning and pixel-based methods. They have been exploited to obtain land and water classes which have been used as an input data for parameter estimation. Thus, the performance of different segmentation algorithm has been investigated and analysed. In the first step of the study, Mean-Shift, Random Forest and Whale Optimization algorithms have been employed to obtain water and land classes from the SENTINEL-2A image. Water and land classes derived from each algorithm – are used as input data, and then the required parameters for the fuzzy clustering of SENTINEL-1A SAR image, were calculated. Lake Constance, Germany has been chosen as the study area. In this study, additionally an interface plugin has been developed and integrated into the open source Quantum GIS software platform. The developed interface allows non-experts to process and extract the shorelines without using any parameters. But, this system requires pre-segmented data as input. Thus, the batch process calculates the required parameters

Correction to: Accuracy and Prognostic Role of NCCT-ASPECTS Depend on Time from Acute Stroke Symptom-onset for both Human and Machine-learning Based Evaluation.

PURPOSE: We hypothesize that the detectability of early ischemic changes on non-contrast computed tomography (NCCT) is limited in hyperacute stroke for both human and machine-learning based evaluation. In short onset-time-to-imaging (OTI), the CT angiography collateral status may identify fast stroke progressors better than early ischemic changes quantified by ASPECTS. METHODS: In this retrospective, monocenter study, CT angiography collaterals (Tan score) and ASPECTS on acute and follow-up NCCT were evaluated by two raters. Additionally, a machine-learning algorithm evaluated the ASPECTS scale on the NCCT (e-ASPECTS). In this study 136 patients from 03/2015 to 12/2019 with occlusion of the main segment of the middle cerebral artery, with a defined symptom-onset-time and successful mechanical thrombectomy (MT) (modified treatment in cerebral infarction score mTICI = 2c or 3) were evaluated. RESULTS: Agreement between acute and follow-up ASPECTS were found to depend on OTI for both human (Intraclass correlation coefficient, ICC = 0.43 for OTI < 100 min, ICC = 0.57 for OTI 100–200 min, ICC = 0.81 for OTI ≥ 200 min) and machine-learning based ASPECTS evaluation (ICC = 0.24 for OTI < 100 min, ICC = 0.61 for OTI 100–200 min, ICC = 0.63 for OTI ≥ 200 min). The same applied to the interrater reliability. Collaterals were predictors of a favorable clinical outcome especially in hyperacute stroke with OTI < 100 min (collaterals: OR = 5.67 CI = 2.38–17.8, p < 0.001; ASPECTS: OR = 1.44, CI = 0.91–2.65, p = 0.15) while ASPECTS was in prolonged OTI ≥ 200 min (collaterals OR = 4.21,CI = 1.36–21.9, p = 0.03; ASPECTS: OR = 2.85, CI = 1.46–7.46, p = 0.01). CONCLUSION: The accuracy and reliability of NCCT-ASPECTS are time dependent for both human and machine-learning based evaluation, indicating reduced detectability of fast stroke progressors by NCCT. In hyperacute stroke, collateral status from CT-angiography may help for a better prognosis on clinical outcome and explain the occurrence of futile recanalization. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00062-021-01110-5) contains supplementary material, which is available to authorized users

Accuracy and Prognostic Role of NCCT-ASPECTS Depend on Time from Acute Stroke Symptom-onset for both Human and Machine-learning Based Evaluation.

PURPOSE We hypothesize that the detectability of early ischemic changes on non-contrast computed tomography (NCCT) is limited in hyperacute stroke for both human and machine-learning based evaluation. In short onset-time-to-imaging (OTI), the CT angiography collateral status may identify fast stroke progressors better than early ischemic changes quantified by ASPECTS. METHODS In this retrospective, monocenter study, CT angiography collaterals (Tan score) and ASPECTS on acute and follow-up NCCT were evaluated by two raters. Additionally, a machine-learning algorithm evaluated the ASPECTS scale on the NCCT (e-ASPECTS). In this study 136 patients from 03/2015 to 12/2019 with occlusion of the main segment of the middle cerebral artery, with a defined symptom-onset-time and successful mechanical thrombectomy (MT) (modified treatment in cerebral infarction score mTICI = 2c or 3) were evaluated. RESULTS Agreement between acute and follow-up ASPECTS were found to depend on OTI for both human (Intraclass correlation coefficient, ICC = 0.43 for OTI < 100 min, ICC = 0.57 for OTI 100-200 min, ICC = 0.81 for OTI ≥ 200 min) and machine-learning based ASPECTS evaluation (ICC = 0.24 for OTI < 100 min, ICC = 0.61 for OTI 100-200 min, ICC = 0.63 for OTI ≥ 200 min). The same applied to the interrater reliability. Collaterals were predictors of a favorable clinical outcome especially in hyperacute stroke with OTI < 100 min (collaterals: OR = 5.67 CI = 2.38-17.8, p < 0.001; ASPECTS: OR = 1.44, CI = 0.91-2.65, p = 0.15) while ASPECTS was in prolonged OTI ≥ 200 min (collaterals OR = 4.21,CI = 1.36-21.9, p = 0.03; ASPECTS: OR = 2.85, CI = 1.46-7.46, p = 0.01). CONCLUSION The accuracy and reliability of NCCT-ASPECTS are time dependent for both human and machine-learning based evaluation, indicating reduced detectability of fast stroke progressors by NCCT. In hyperacute stroke, collateral status from CT-angiography may help for a better prognosis on clinical outcome and explain the occurrence of futile recanalization

EFFECT OF DIFFERENT SEGMENTATION METHODS USING OPTICAL SATELLITE IMAGERY TO ESTIMATE FUZZY CLUSTERING PARAMETERS FOR SENTINEL-1A SAR IMAGES

Optical and SAR data are efficient data sources for shoreline monitoring. The processing of SAR data such as feature extraction is not an easy task since the images have totally different structure than optical imagery. Determination of threshold value is a challenging task for SAR data. In this study, SENTINEL-2A optical data was used as ancillary data to predict fuzzy membership parameters for segmentation of SENTINEL-1A SAR data to extract shoreline. SENTINEL-2A and SENTINEL-1A satellite images used were taken in September 9, 2016 and September 13, 2016 respectively. Three different segmentation algorithms which are selected from object, learning and pixel-based methods. They have been exploited to obtain land and water classes which have been used as an input data for parameter estimation. Thus, the performance of different segmentation algorithm has been investigated and analysed. In the first step of the study, Mean-Shift, Random Forest and Whale Optimization algorithms have been employed to obtain water and land classes from the SENTINEL-2A image. Water and land classes derived from each algorithm – are used as input data, and then the required parameters for the fuzzy clustering of SENTINEL-1A SAR image, were calculated. Lake Constance, Germany has been chosen as the study area. In this study, additionally an interface plugin has been developed and integrated into the open source Quantum GIS software platform. The developed interface allows non-experts to process and extract the shorelines without using any parameters. But, this system requires pre-segmented data as input. Thus, the batch process calculates the required parameters

New method to characterize a machining system: application in turning

Many studies simulates the machining process by using a single degree of freedom spring-mass sytem to model the tool stiffness, or the workpiece stiffness, or the unit tool-workpiece stiffness in modelings 2D. Others impose the tool action, or use more or less complex modelings of the efforts applied by the tool taking account the tool geometry. Thus, all these models remain two-dimensional or sometimes partially three-dimensional. This paper aims at developing an experimental method allowing to determine accurately the real three-dimensional behaviour of a machining system (machine tool, cutting tool, tool-holder and associated system of force metrology six-component dynamometer). In the work-space model of machining, a new experimental procedure is implemented to determine the machining system elastic behaviour. An experimental study of machining system is presented. We propose a machining system static characterization. A decomposition in two distinct blocks of the system "Workpiece-Tool-Machine" is realized. The block Tool and the block Workpiece are studied and characterized separately by matrix stiffness and displacement (three translations and three rotations). The Castigliano's theory allows us to calculate the total stiffness matrix and the total displacement matrix. A stiffness center point and a plan of tool tip static displacement are presented in agreement with the turning machining dynamic model and especially during the self induced vibration. These results are necessary to have a good three-dimensional machining system dynamic characterization